import argparse, os, sys, glob import torch import numpy as np from omegaconf import OmegaConf from PIL import Image from tqdm import tqdm, trange from itertools import islice from einops import rearrange from torchvision.utils import make_grid, save_image import time from pytorch_lightning import seed_everything from torch import autocast from contextlib import contextmanager, nullcontext from ldm.util import instantiate_from_config from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.plms import PLMSSampler def chunk(it, size): it = iter(it) return iter(lambda: tuple(islice(it, size)), ()) def load_model_from_config(config, ckpt, verbose=False): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") print(m) if len(u) > 0 and verbose: print("unexpected keys:") print(u) model.cuda() model.eval() return model def main(text): parser = argparse.ArgumentParser() # parser.add_argument( # "--prompt", # type=str, # nargs="?", # default="a painting of a virus monster playing guitar", # help="the prompt to render" # ) # parser.add_argument( # "--outdir", # type=str, # nargs="?", # help="dir to write results to", # default="outputs/txt2img-samples" # ) parser.add_argument( "--skip_grid", action='store_true', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples", ) parser.add_argument( "--skip_save", action='store_false', help="do not save individual samples. For speed measurements.", ) parser.add_argument( "--ddim_steps", type=int, default=100, help="number of ddim sampling steps", ) parser.add_argument( "--plms", action='store_true', help="use plms sampling", ) parser.add_argument( "--laion400m", action='store_true', help="uses the LAION400M model", ) parser.add_argument( "--fixed_code", action='store_true', help="if enabled, uses the same starting code across samples ", ) parser.add_argument( "--ddim_eta", type=float, default=0.0, help="ddim eta (eta=0.0 corresponds to deterministic sampling", ) parser.add_argument( "--n_iter", type=int, default=1, help="sample this often", ) parser.add_argument( "--H", type=int, default=512, help="image height, in pixel space", ) parser.add_argument( "--W", type=int, default=512, help="image width, in pixel space", ) parser.add_argument( "--C", type=int, default=4, help="latent channels", ) parser.add_argument( "--f", type=int, default=8, help="downsampling factor", ) parser.add_argument( "--n_samples", type=int, default=4, help="how many samples to produce for each given prompt. A.k.a. batch size", ) parser.add_argument( "--n_rows", type=int, default=0, help="rows in the grid (default: n_samples)", ) parser.add_argument( "--scale", type=float, default=10., help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", ) parser.add_argument( "--from-file", type=str, help="if specified, load prompts from this file", ) parser.add_argument( "--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model", ) parser.add_argument( "--ckpt", type=str, default="./sd-v1-4-full-ema.ckpt", help="path to checkpoint of model", ) parser.add_argument( "--seed", type=int, default=42, help="the seed (for reproducible sampling)", ) parser.add_argument( "--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast" ) parser.add_argument( "--embedding_path", type=str, help="Path to a pre-trained embedding manager checkpoint") opt = parser.parse_args() opt.prompt = text if opt.laion400m: print("Falling back to LAION 400M model...") opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml" opt.ckpt = "models/ldm/text2img-large/model.ckpt" # opt.outdir = "outputs/txt2img-samples-laion400m" seed_everything(opt.seed) config = OmegaConf.load(f"{opt.config}") model = load_model_from_config(config, f"{opt.ckpt}") #model.embedding_manager.load(opt.embedding_path) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model = model.to(device) if opt.plms: sampler = PLMSSampler(model) else: sampler = DDIMSampler(model) # os.makedirs(opt.outdir, exist_ok=True) # outpath = opt.outdir batch_size = opt.n_samples n_rows = opt.n_rows if opt.n_rows > 0 else batch_size if not opt.from_file: prompt = opt.prompt assert prompt is not None data = [batch_size * [prompt]] else: print(f"reading prompts from {opt.from_file}") with open(opt.from_file, "r") as f: data = f.read().splitlines() data = list(chunk(data, batch_size)) # sample_path = os.path.join(outpath, "samples") # os.makedirs(sample_path, exist_ok=True) # base_count = len(os.listdir(sample_path)) # grid_count = len(os.listdir(outpath)) - 1 start_code = None if opt.fixed_code: start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device) precision_scope = autocast if opt.precision=="autocast" else nullcontext with torch.no_grad(): with precision_scope("cuda"): with model.ema_scope(): tic = time.time() all_samples = list() for n in trange(opt.n_iter, desc="Sampling"): for prompts in tqdm(data, desc="data"): uc = None if opt.scale != 1.0: uc = model.get_learned_conditioning(batch_size * [""]) if isinstance(prompts, tuple): prompts = list(prompts) c = model.get_learned_conditioning(prompts) shape = [opt.C, opt.H // opt.f, opt.W // opt.f] samples_ddim, _ = sampler.sample(S=opt.ddim_steps, conditioning=c, batch_size=opt.n_samples, shape=shape, verbose=False, unconditional_guidance_scale=opt.scale, unconditional_conditioning=uc, eta=opt.ddim_eta, x_T=start_code) x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) # if not opt.skip_save: # for x_sample in x_samples_ddim: # x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') # Image.fromarray(x_sample.astype(np.uint8)).save( # os.path.join(sample_path, f"{base_count:05}.jpg")) # base_count += 1 if not opt.skip_grid: all_samples.append(x_samples_ddim) if not opt.skip_grid: # additionally, save as grid grid = torch.stack(all_samples, 0) grid = rearrange(grid, 'n b c h w -> (n b) c h w') # for i in range(grid.size(0)): # save_image(grid[i, :, :, :], os.path.join(outpath,opt.prompt+'_{}.png'.format(i))) grid = make_grid(grid, nrow=n_rows) # to image grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy().astype(np.uint8) # Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'{prompt.replace(" ", "-")}-{grid_count:04}.jpg')) # grid_count += 1 toc = time.time() return grid # print(f"Your samples are ready and waiting for you here: \n{outpath} \n" # f" \nEnjoy.") if __name__ == "__main__": main()